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Abstract

In Italy, nuclear power is currently on the agenda of the national government. After abandoning nuclear energy 20 years ago, Italy has not had any direct experience since. Besides its social acceptance and the problem of handling radioactive waste, the economic appraisal of nuclear energy compared to coal and gas is the most controversial item in the current debate on energy resources. Unfortunately, the figures published in the literature comparing the competitiveness of nuclear energy against fossil fuels differ wildly. This inconsistency demonstrates the uncertainties in the evaluation process. This paper develops an approach based on fuzzy-sets to handle the uncertainty of nuclear power costs. The underlying aim is to appraise the economic advantages of nuclear power compared with other traditional energy sources, namely coal and natural gas. Having illustrated the general state of the art of nuclear power in terms of production, installed power and economics, a more detailed evaluation follows to select the most important economic and financial parameters involved in calculating the industrial cost (overnight cost, O&M, fuel, etc.) to then build the fuzzy functions and process them using the fuzzy TOPSIS method in order to obtain a final ranking of alternatives analyzed.

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1. Introduction

In July 2009, more than 20 years after the referendum repealing nuclear power, the Italian government proposed a bill to reintroduce nuclear power in Italy1. In addition to the objections on environmental grounds and social acceptance, this decision has raised many questions mainly for economic and financial reasons. Having abandoned the nuclear industry for such a long time, Italy is now in a weak position as regards infrastructure and has a technology gap, which could lead to expectations of cheap and easily producible nuclear power being seriously disappointed. Besides its social acceptance and the problem of handling radioactive waste, the economic appraisal of nuclear energy compared to coal and gas, its main competitors, is the most controversial item in the current debate on energy resources. Who would be prepared to invest in nuclear power in Italy? Who should fund the operation? What are the economic and financial risks involved? How long would it take to build the first reactor in Italy and how much would this cost? All questions to which answers have yet to be given.

The most prestigious international research bodies and universities have undertaken significant studies on costs and competitiveness. However, the conclusions drawn have very often been discordant and the inconsistent findings hint that estimating costs of nuclear energy is a task beset with uncertainty. The lack of consistent values applied to the various parameters (overnight cost, O&M cost, lifetime of plant, etc) means that the main problem is choosing which of these to use for the parameters employed in the appraisal of energy technologies. How can this uncertainty be handled in real terms? The most important papers in the literature that deal with the uncertainty of energy production costs are based on stochastic processes. A probability model was devised by Feretic and Tomsic (2005) and De Paoli and Gullì (2008) in order to overcome this ambiguity in part using the Monte Carlo analysis method. Others (Gollier et al., 2005; Naito et al., 2010; Takizawa & Suzuki, 2004) have contributed interesting ideas regarding appraisal under conditions of uncertainty.

This paper sets out another approach for dealing with uncertainty of costs based on fuzzy logic. The advantage of using fuzzy sets lies in its ability to handle uncertainty more easily and more transparently than traditional tools. Fuzzy sets capture the notion of “possibility” which is a wider concept than that of “probability”. A fuzzy-set is an estimate of an uncertain parameter with a value that may vary within a possible range while a probabilistic estimate implies forecasting a probable value. In our case, the use of fuzzy-sets thereby allowed us to represent the often divergent values of each parameter using a data interval (min-max).